Frailty trajectories preceding dementia: an individual-level analysis of four cohort studies in the United States and United Kingdom

Frailty may represent a modifiable risk factor for dementia, but the direction of that association remains uncertain. We investigated frailty trajectories in the years preceding dementia onset using data from 23,672 participants (242,760 person-years of follow-up, 2,906 cases of incident dementia) across four cohort studies in the United States and United Kingdom. Bayesian non-linear models revealed accelerations in frailty trajectories 4–9 years before incident dementia. Among participants whose time between frailty measurement and incident dementia exceeded that prodromal period, frailty remained positively associated with dementia risk (adjusted hazard ratios ranged from 1.20 [95% confidence interval, CI = 1.15–1.26] to 1.43 [95% CI = 1.14–1.81]). This observational evidence suggests that frailty increases dementia risk independently of any reverse causality. These findings indicate that frailty measurements can be used to identify high-risk population groups for preferential enrolment into clinical trials for dementia prevention and treatment. Frailty itself may represent a useful upstream target for behavioural and societal approaches to dementia prevention.


INTRODUCTION
Research on dementia causes is dominated by Alzheimer's disease, which focuses on singular disease mechanisms that do not account for symptomology in most cases 1,2 .However, as dementia most commonly arises in older people with mixed age-related neuropathologies 1,2 , processes of ageing are implicated in shaping disease susceptibility.This perspective is supported by evidence linking changes in the biological hallmarks of ageing with differences in dementia risk and has given rise to the development of novel anti-ageing approaches to neurodegenerative conditions 3,4 , for which phase 1 trial results are now being reported 5 .In addition to informing drug discovery, better understanding the complex relationship between ageing and late-life dementia may be leveraged into behavioural and societal approaches to dementia prevention.For the optimal development of such approaches, a readily measurable target that captures biological age and causally associates with incident dementia is required.Accumulating evidence indicates that frailty may be a viable candidate for that role [6][7][8] .
Frailty can be understood as a gradable health state that increases risk for adverse health outcomes independently of chronological age and re ects differences in the accumulation of age-related health de cits 9 .At any age, a higher degree of frailty is associated with higher all-cause mortality to a greater degree than are common lab-based estimates of biological age 7,10 .Assessing frailty may therefore provide an accessible means of estimating biological age 6,9 , with broad relevance to disease prognosis and care planning 7 .Epidemiological reports using data from independent cohorts have consistently shown that dementia occurs more frequently among those individuals who have a higher degree of frailty 8, [11][12][13][14] .These associations persist after adjusting for chronological age and other possible confounding factors, such as sex and educational attainment.Even so, the current evidence base falls short of allowing a causal interpretation of the association of frailty with dementia due to the unresolved possibility of reverse causality.For example, Alzheimer's disease is thought to have a long preclinical phase (up to 15-20 years) 15,16 , with subtle changes in health, function and behaviour detectable in the years prior to dementia diagnosis [16][17][18][19][20] .Therefore, among people assessed as being without dementia at the time of frailty measurement, subclinical changes in health and function may already be re ected as a higher degree of frailty and consequently confound the subsequent detection of a causal relationship between frailty and incident dementia.
In the absence of randomised controlled trials, cohort studies together with statistical approaches using backwards timescales can detail the temporal nature of dementia risk factors with dementia onset 21,22 .
That approach and investigation of its consequences on risk associations have not yet been applied to frailty.Understanding the dynamics of frailty trajectories in the years before dementia can test frailty as an upstream target in efforts to reduce dementia incidence.It may also inform optimal approaches to the targeted recruitment of high-risk populations into clinical trials for dementia prevention and treatment.Using four cohort studies of health, cognition and ageing, we aimed to clarify the relationship between frailty and incident dementia while considering the possibility of reverse causality.To achieve this, we pursued two objectives: (1) determine when an acceleration in the accumulation of frailty due to impending dementia is rst observable and (2) measure the association of frailty and dementia risk after controlling for any impact of that pre-dementia frailty acceleration period.The null hypothesis is that any increased risk of dementia in relation to frailty would not hold when frailty measurement occurred before the pre-dementia frailty acceleration period.

Datasets
We analysed participant data from four large cohort studies: the English Longitudinal Study of Ageing (ELSA), Health and Retirement Study (HRS), Rush Memory and Aging Project (MAP), and National Alzheimer's Coordinating Center (NACC).ELSA is a longitudinal panel study of a representative sample of community-dwelling adults aged 50 years or older in England 23 .HRS, a longitudinal panel study, surveys a representative sample of older adults in the United States 24 .MAP is a clinical-pathological cohort study of older adults in Illinois, United States 25 .NACC collects participant data contributed by Alzheimer's Disease Research Centers (ADRCs) in the United States using standardised methods 26 .Details of study methodology and data access are included in Supplementary Information 1.
Participants were included if they were aged 60 years or over at baseline, were without cognitive impairment, had data available on age, sex and education level, had some follow-up data, and had su cient data to calculate a frailty index score at baseline assessment and at least one additional timepoint prior to incident dementia or censoring (Fig. 1).Frailty index scores were only calculated where participants had information available on at least 30 de cits used in that study's frailty index 27 .To remove the in uence of early-onset dementia cases that often occur exclusively due to genetic causes 28 , participants were also excluded if they developed dementia before age 65 years.

Incident dementia
Given that mixed dementia is what occurs chie y in late life 1,2 , the study outcome was all-cause dementia.The method of determining this outcome differed between studies.In ELSA, classi cations were derived through either a self-report of physician diagnosis of dementia or a mean score of ≥ 3.4 on the 16-item Informant Questionnaire on Cognitive Decline in Elderly (IQCODE) completed by family members/caregivers, which represents a decline in the ability of daily function compared to two years prior of a magnitude indicating dementia 29 .In HRS, classi cations of dementia were obtained using the Langa-Weir Classi cation of Cognitive Function method, which applies validated cut-points to summary scores obtained from a range of cognitive tests (scores ranged from 0-27; scores of 0-6 indicated the presence of dementia) 30 .In MAP, presumptive diagnoses of dementia and Alzheimer's disease were calculated via an algorithmic decision tree using accepted clinical criteria and con rmed by a clinician 31 .
In NACC, either a consensus team or a single physician used standard diagnostic criteria to classify participants as having all-cause dementia 31,32 .

Frailty measurement
Frailty was the main exposure in this study, with each participant's degree of frailty quanti ed using retrospectively calculated frailty index scores.The frailty index approach was used due to its value in predicting adverse health outcomes relative to other common approaches to frailty assessment 33 .The frailty index is a measure of health state, combining information from multiple physiological systems and closely re ecting an individual's risk for adverse health events and mortality independently of chronological age 9 .The health variables included in a frailty index are routinely collected clinical data such as symptoms, signs, disabilities and diseases that meet standard criteria 27 .As frailty index scores represent the proportion of total health de cits of an individual, higher scores indicate the accumulation of more age-related health de cits and worse health.For example, a person with 15 of 50 assessed health de cits has a frailty index score of 15/50 = 0.3.
Frailty index scores had been developed and validated previously in each cohort 13,[34][35][36][37] .Although these scores are generated from frailty indices composed of different health and functional de cits, frailty can be measured reliably if multiple physiological/functional domains are represented and if enough de cits (e.g. more than 30) are included 27,38 .Fewer items can be included but the information reduces and measurement error increases accordingly 39,40 .Where necessary, each frailty index was adapted for our investigation by ensuring that de cits closely re ecting cognition were removed from their composition, such as the diagnosis of a neurodegenerative disease or a measure of cognitive performance (Supplementary Table 1).For use in sensitivity analyses under objective 2 (i.e. when measuring the association of frailty and incident dementia), we calculated a second frailty index where we excluded de cits that were found to be independently associated with incident dementia based on analyses in each dataset.
Prior to using frailty index scores in survival models, the scores were multiplied by 10 so that hazard ratios could be meaningfully interpreted as the change in dementia risk associated with each 0.1 increase in frailty index scores.

Covariates
Consistent with previous work, participant age, sex and education level were included as covariates due to possibly confounding the relationship between frailty and incident dementia 13 .In all datasets, age was measured in years at baseline; sex was a self-reported binary variable (male/female); education was reported at baseline and for consistency between studies was recoded into a three-category variable (lower, intermediate and higher education).In ELSA, higher education was completion of a higher education quali cation below a degree, or a degree or equivalent.Intermediate education was completion of a CSE, GCE O, GCE A or equivalent, and lower education was no formal quali cation.In HRS, higher education was completion of an associate's degree, bachelor's degree, master's degree, PhD or similar.
Intermediate education was completion of a high school diploma or GED, and lower education was no formal quali cation.In MAP and NACC, higher education was more than 12 years of formal education, intermediate education was 10, 11 or 12 years of formal education, and lower education was less than 10 years of formal education.Information regarding mortality data, which were used in censoring, is included in Supplementary Information 2.

Sample characteristics
The demographic characteristics of participants at baseline in each study were rst summarised using descriptive statistics.

Objective 1
To determine when an acceleration in the accumulation of frailty associated with impending dementia is rst observable (objective 1), we modelled trajectories in frailty index scores (the dependent variable) using a backwards timescale.Here, a time value equalling zero was the year of incident dementia or censor and negative time values represented the number of years until that event.This approach has been used by others when exploring trajectories of dementia risk factors prior to dementia development 21,22 .
For this process, we used the Bayesian Regression Models using 'Stan' (brms) package in R to t Bayesian generalised non-linear multilevel models 41 .In each model, population-level effects of time were tted using natural cubic splines, which allow for non-linear trajectories in frailty index scores (e.g.rate of increase in frailty may hasten with advancing age 42 ), and included both a random intercept and slope (linear t) for participants.Preliminary models showed that six degrees of freedom ( ve knots) were appropriate parameters for the natural cubic spline of time; this aligns with recommendations that including more than six degrees of freedom in splines is often unnecessary even for large datasets (as analysed here) 43 .Given the non-negative and right-skewed distribution of frailty index scores, we used the gamma distribution with a log link function.
A base model was rst built that included xed effects of time, event group (incident dementia or censored) and possible confounders (age, sex, education).We then built an interaction model to include an additional xed effect representing the interaction between time (natural cubic spline) and event group (incident dementia or censored).This event group x time interaction term allowed the association of time and frailty index scores to vary by event group.Fit was compared between these two models to assess whether frailty trajectories differed between incident dementia and censored participants, with differencein-t statistics accompanied by 95% credible intervals to assist interpretation.From the interaction model, we assessed the marginal effect of event group on frailty trajectories by calculating expected frailty index scores for each participant at each time point while holding the other covariates constant (i.e. at each sample's median age and the most frequently occurring level of each factor).These expected scores were plotted as trajectories strati ed by dementia group.For greater speci city regarding the time point after which frailty accumulation consistently accelerated due to impending dementia, we calculated mean differences in expected scores by dementia group at each time point (rounded to nearest whole years) and tested these using t-tests.We estimated the start of the pre-dementia frailty acceleration period as the year after which the size of differences in frailty index scores between the incident dementia group and the censored group were observed to be statistically signi cant and increase consistently.
Convergence of four chains with each 3,000 iterations (excluding 500 warm-up iterations) under weakly informative priors was con rmed by inspection of trace plots and R-hat values.Standard model diagnostic tools (e.g.posterior predictive checks) were used to con rm the suitability of the modelling approach.Expected log pointwise predictive density (elpd) leave-one-out (loo) cross-validation was used to assess and compare model t in all cases.
For objective 1, we de ned the follow-up period as beginning at participants' baseline assessments and continuing until incident dementia.In individuals who did not develop dementia, the follow-up ended three years before death or at the last date at which they were known to be without dementia, whichever came rst.The three-year censoring rule was implemented to improve the comparison between frailty trajectories before incident dementia and frailty trajectories in normal ageing; this exclusion takes into account the known ve-fold increase in the rate of health de cit accumulation that occurs within the last three years of life (often referred to as the "terminal decline" phase) 34 .However, the three-year censoring rule could not be applied to ELSA due to unavailable mortality data (Supplementary Information 2).

Objective 2
We next measured the association of frailty and incident dementia after controlling for any impact of the pre-dementia frailty acceleration period (objective 2).To do this, we rst used Cox proportional hazards models to examine the relationships between frailty index scores and dementia risk while adjusting for possible confounders (age, sex and education) in the total samples.This model was then estimated separately within two subgroups.The rst subgroup included participants whose time between baseline frailty measurement and event (incident dementia, censor) was less than or equal to the pre-dementia frailty acceleration period (as estimated in objective 1).The second subgroup included participants whose time between baseline frailty measurement and event was greater than the pre-dementia frailty acceleration period.Differences in the associations of frailty index scores with dementia risk between these groups were then quanti ed using interaction terms.Relationships were expressed as hazard ratios (HRs) and accompanied by 95% con dence intervals (CIs).For objective 2, the follow-up period additionally included the observations within three years of death for individuals who did not develop dementia.

Sensitivity analyses
All statistical results were determined within the overall datasets and then within males and females, separately, within each dataset.These sex-strati ed results are presented in Supplementary Figs.1-4.For objective 2, two sensitivity analyses were conducted to assess the robustness of associations of frailty index scores and incident dementia.First, to ensure that the pre-dementia frailty acceleration period was not being systematically underestimated, it was increased by two years and analyses were repeated.
Second, to reduce the potential that the inclusion of possibly confounding health de cits drove associations, analyses were repeated using a second frailty index that additionally excluded de cits shown to be independently associated (P < 0.05) with incident dementia in multivariable Cox proportional hazards models adjusted for age, sex, education and all other de cits.

Analytical approach
We used a coordinated approach whereby the structure of datasets was rst made consistent before an identical analytical procedure (Supplementary Analysis Script) was applied to generate summary statistics, statistical results and gures.All statistical analyses were conducted using R V.4.2.1.

Sample characteristics
Data from 23,672 participants (62% female) were included in this analysis (Table 1).Most participants were contributed by NACC (42%) and least by MAP (5%).In total, 242,760 person-years of follow-up and 2,906 cases of incident dementia were analysed.Among the cohorts, participants in MAP were oldest and had the highest degrees of frailty, on average, corresponding to the highest observed rates of incident dementia.

Frailty trajectories prior to dementia
To determine when an acceleration in the accumulation of frailty associated with impending dementia might be rst observable (objective 1), we modelled frailty index scores using backwards timescales and adjusted for potential confounders.In the years before incident dementia or censor, frailty index scores tended to increase (Fig. 2).Among the censored groups, gradual increases in frailty index scores were observed in all datasets, although these were smallest in NACC.Among the incident dementia groups, we observed accelerations in the rates of increase in frailty index scores in the years proximal to dementia.
These were particularly pronounced in ELSA and NACC, and less so in MAP and HRS, although still present in those datasets.That divergence in frailty trajectories associated with incident dementia was supported by the model results, whereby, for all datasets, the inclusion of an event group (incident dementia or censored) by time interaction term resulted in improved model t (Table 2).The populationlevel effects from the interaction model (i.e. that which included the event group by time interaction term) are presented in Supplementary Table 2.
Expected frailty index scores, calculated from the interaction model while holding the covariates of age, sex and education constant, were then compared between the incident dementia and censored groups at each year (Fig. 2).Compared with the censored groups, these frailty scores were consistently higher in the incident dementia groups, 20, 12, 12, and 8 years before dementia in HRS, ELSA, MAP and NACC, respectively.At the point of dementia detection, frailty index scores were most elevated in ELSA (0.19 points higher than censored participants), elevated to a similar degree in both MAP and NACC (0.12 points higher), and to a lesser extent in HRS (0.04 points higher).The start of the pre-dementia frailty acceleration period, i.e. the year after which the size of differences in frailty index scores between the incident dementia group and the censored group were observed to be statistically signi cant and increase consistently, was estimated at 9, 6, 4 and 4 years before dementia for NACC, MAP, ELSA and HRS, which was similar in both males and females (Supplementary Figs.1-4).The mean differences in expected frailty index scores and associated P values are presented in Supplementary Table 3.

Frailty and incident dementia
We next measured the of frailty index scores and incident dementia after controlling for the pre-dementia frailty acceleration period (objective 2).This we did by using Cox proportional-hazards models to determine the associations of frailty with incident dementia for participants whose time between baseline frailty measurement and event (incident dementia or censored) was greater than the cohort-speci c pre-dementia frailty acceleration period (as estimated under objective 1).The size of analysed samples, the pre-dementia frailty acceleration periods, and the number of de cits included in frailty indices varied in the main and sensitivity analyses (Table 3).Note: The pre-dementia frailty acceleration period was estimated as the year after which the size of differences in frailty index scores between the incident dementia group and the censored group were observed to be statistically signi cant and increase consistently.S1, sensitivity analysis 1, in which the pre-dementia frailty acceleration period was increased by two years; S2, sensitivity analysis 2, whereby de cits found to be independently associated (P < 0.05) with incident dementia were removed from the calculation of frailty index scores.For S2, participants who did not have data on at least 30 items included in the second frailty index were excluded.ELSA, English Longitudinal Study of Ageing; HRS, Health and Retirement Study; MAP, Rush Memory and Aging Project; NACC, National Alzheimer's Coordinating Center.
In the main analyses, in each dataset, each 0.1 increase in frailty index scores (equivalent to 4-5 additional health de cits) was associated with higher dementia risk (Fig. 3).This association was strongest in NACC (70% increase in risk), weakest in HRS (21% increase in risk), and similar in ELSA (31% increase in risk) and MAP (36% increase in risk).
When the time between frailty measurement and incident dementia or censor was considered, associations remained similar in both groups (i.e. in participants whose time between frailty measurement and incident dementia or censor was less than or equal to the pre-dementia frailty acceleration period, and in participants whose time between measurement and outcome exceeded that period).Here, event timing x frailty index score interaction terms were not statistically signi cant in ELSA (P = 0.921), HRS (P = 0.205), MAP (P = 0.411) or NACC (P = 0.733).Across datasets and in participants whose baseline frailty measurement was conducted before the pre-dementia acceleration period had begun, the associations of frailty index scores with dementia risk were consistently positive and statistically signi cant.There, each 0.1 increase in frailty index scores was associated with 20-43% increased dementia risk, and in the absence of meaningful differences in this association between males and females (Supplementary Figs.1-4).The results from both sensitivity analyses demonstrated a robustness in these ndings, whereby frailty index scores calculated before the pre-dementia frailty acceleration period remained associated with incident dementia at a statistically signi cant level even when that period was extended by two years (sensitivity analysis 1).Likewise, our results were robust to removing health de cits that were independently associated with incident dementia from the calculation of frailty index scores (sensitivity analysis 2).

DISCUSSION
With the purpose of addressing reverse causality in the relationship between frailty and dementia, we identi ed the point at which frailty accelerated prior to dementia onset and determined how the timing of frailty measurement relative to that point affected the strength of risk associations.From this analysis of almost 24,000 individuals participating in four cohort studies in the United Kingdom and United States, report three main ndings: 1) an elevated degree of frailty was observed 8 to 20 years before dementia onset; 2) the rate of decline in health and function in prodromal dementia, as re ected in a higher degree of frailty, accelerated from 4-9 years before dementia onset; 3) frailty was a robust risk factor for incident dementia even when its measurement occurred before the pre-dementia frailty acceleration period.These results offer insight into the natural course of declining health in the subclinical stages of neurodegenerative diseases, position frailty index scores as a measure effective in identifying high-risk individuals for inclusion into treatment and prevention trials for dementia, and substantially strengthen the evidence for frailty serving as an upstream dementia risk factor.
Previous reports have suggested a preclinical phase of Alzheimer's disease up to 15-20 years in length 15,16 , with changes in health and function rst detectable at a population level from 10 years before dementia onset.Examples of these include higher health care usage and lower social engagement (2 years prior to diagnosis) 17,18 , accelerated cognitive decline (6-10 years prior) 16,19 , and more depressive symptoms (10 years prior) 20 .Instead of assuming a static in ection point for prodromal dementia in our attempts to investigate reverse causality, here we determined them dynamically within each dataset by modelling frailty trajectories.Even though we observed a degree of heterogeneity in frailty trajectories between the datasets, in each case the pre-dementia frailty acceleration period was estimated to lie within that 10-year prodromal period (ranging from 4-9 years), supporting those earlier studies.Consequently, one explanation for elevated frailty in the years proximal to dementia relates to the adverse impacts of neurodegenerative changes.
Aside from neurodegenerative processes hastening frailty accumulation, another explanation for our ndings is that accelerated biological ageing is a dementia cause rather than consequence.In support, strong links have been established between changes in the hallmarks of ageing and the development of neurodegenerative diseases 3,4 , and chronological age itself has long been understood as a key risk factor.Rapidly increasing frailty index scores, observed here up to 9 years before dementia onset, may therefore signal an exhaustion of systemic reserves leaving affected individuals vulnerable to diseases that might otherwise have remained subclinical 9 .This loss of reserve associated with higher frailty has been demonstrated previously in dementia, where frailty was associated with weaker relationships between dementia and neuropathological burden and polygenic risk despite persistently high dementia rates 8, 44,45 .
Regardless of the nature of the relationship between the pre-dementia frailty acceleration period and subsequent dementia, the ndings from our time-to-event analyses align with the position that frailty is a strong risk factor for dementia and that the relationship between frailty and dementia does not exclusively re ect reverse causality.In individuals whose measurement of frailty occurred before the predementia frailty acceleration period had begun, and in both males and females, we observed each 0.1 increase in frailty index scores to increase dementia risk substantially.The strength of those associations with risk either remained the same (ELSA, HRS) or increased (MAP, NACC) in a sensitivity analysis that extended the pre-dementia frailty acceleration period by two years (sensitivity analysis 1), suggesting that frailty measurement conducted distally to the occurrence of dementia can be used for risk strati cation.
Those associations also remained statistically signi cant in a sensitivity analysis that calculated frailty index scores exclusively using de cits that were not independently associated with incident dementia (sensitivity analysis 2).Our ndings join previous reports of a robust association between frailty and incident dementia, even when adjusting for a polygenic dementia risk score and a marker of area-level deprivation 8 , adjusting for the competing risk of death 12 , including only non-traditional risk factors in the composition of the frailty index 14 , or when conceptualising frailty as a phenotype 11 .

Strengths and
A strength of our investigation was the use of four different cohort studies across two continents, which varied in participant characteristics and in study methodologies.The setting of studies included retirement communities (MAP), national-level surveys (ELSA, HRS), and a multi clinic-based cohort (NACC), resulting in participant samples diverse in age, education level, degree of frailty, and rates of incident dementia.NACC participants were noteworthy in having the second highest rates of incident dementia despite the lowest degrees of frailty (relative to other cohorts), aligning with the known issues of NACC representativeness relative to the broader United States population (e.g.fewer physical and mental health problems but more subjective cognitive complaints) 46 .The method of dementia detection employed in each study also varied substantially, from physician-derived diagnoses (MAP, NACC), to mostly self-and informant-report (ELSA), and to estimated classi cations based on a combination of cognitive tests (HRS).Some studies used approximately annual interviews/assessments (MAP, NACC) while others were biennial (ELSA, HRS).These differences contributed to variability in our statistical ndings, both in terms of the frailty trajectories and in the strength of associations between frailty index scores and incident dementia.Despite these differences, by applying a consistent analytical approach to each dataset and reviewing results independently, we observed an encouraging consistency in ndings supportive of strong external validity.
Even so, our results should be interpreted with respect to a few limitations.1) We applied a considered approach to reduce the possibility of reverse causality in the association of frailty and dementia, but it is unlikely that it can be ruled out entirely in the absence of a randomised design.Still, associations were observed consistently even when we overestimated the pre-dementia frailty acceleration period by two years.2) For enhanced consistency and comparability in analyses between cohorts, we did not include potentially relevant covariates in statistical models unless they were universally available.Although we included education level, which is an important marker of socioeconomic status, we did not include other markers of social deprivation that may be causally associated with dementia 47 .Similarly, genetic risk for dementia, often approximated using APOE ε4 status, was not adjusted for.Nonetheless, previous reports of strong associations between frailty and incident dementia even after adjusting for social deprivation (e.g.Townsend deprivation index) 8 , and within both APOE ε4 carriers and non-carriers 13 , lead us to maintain con dence in our ndings.3) The included cohort studies were from only two countries (United States and United Kingdom) and a characteristic of most cohort studies is a healthy participant selection bias.The extent to which our ndings apply to non-Western populations, and to populations with fewer social resources and poorer health, is not yet known.

Conclusion
In conclusion, we found robust evidence that frailty increases dementia risk in a manner that appears independent of reverse causality.This study strengthens the evidence base for a causal association by producing novel evidence on the temporality of the relationship between frailty and incident dementia.These ndings suggest that frailty measurements can be used to identify high-risk population groups for preferential enrolment into clinical trials for dementia prevention and treatment, and that frailty itself may represent a useful upstream target for behavioural and societal approaches to dementia prevention.

Declarations Figures
Participant exclusions and analytical samples.ELSA, English Longitudinal Study of Ageing; HRS, Health and Retirement Study; MAP, Rush Memory and Aging Project; NACC, National Alzheimer's Coordinating Center.

Table 1
Proportions may not sum to 100% due to rounding.ELSA, English Longitudinal Study of Ageing; HRS, Health and Retirement Study; MAP, Rush Memory and Aging Project; NACC, National Alzheimer's Coordinating Center.SD, standard deviation.

Table 2
Comparison of t for Bayesian frailty trajectory models Higher values indicate better t.For the base models and interaction models, values in brackets represent standard error.For the difference in t, values in brackets represent 95% credible intervals.The base model included xed effects of time (natural cubic spline), event group, age, sex and education.Model 2 included an additional interaction term between time x event group.Both models included random participant intercepts and slopes.ELSA, English Longitudinal Study of Ageing; HRS, Health and Retirement Study; MAP, Rush Memory and Aging Project; NACC, National Alzheimer's Coordinating Center.

Table 3
Characteristics of frailty and incident dementia risk analyses